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All Journal Multica Science and Technology Jurnal Malikussaleh Mengabdi Jurnal Ilmu Komputer Aceh
Riyadhul Fajri
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Religion Humanities Computer Science & IT Energy Environmental Science Library & Information Science Physics Social Sciences Other

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Pengembangan Teknologi Sistem Pendeteksi Kualitas Tanah Berbasis IoT untuk Peningkatan Produktivitas Hasil Panen Riyadhul Fajri; Salamah; Iqbal; Barokah, Muhammad
Jurnal Malikussaleh Mengabdi Vol. 4 No. 2 (2025): Jurnal Malikussaleh Mengabdi, Oktober 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i02.24675

Abstract

Teknologi Internet of Things (IoT) menawarkan solusi inovatif untuk meningkatkan efektivitas pengukuran suhu tanah melalui pemasangan sensor suhu yang terhubung ke jaringan internet. Pemantauan suhu tanah yang akurat dan real-time merupakan aspek penting dalam bidang pertanian, perkebunan, dan pengelolaan lingkungan. Pemanfaatan Internet of Things (IoT) dalam sistem monitoring pengukuran suhu tanah memainkan peranan penting dalam meningkatkan produktivitas pertanian dan memberikan kontribusi pada konservasi lingkungan. Melalui desain sistem pemantauan berbasis IoT, sensor-sensor berkualitas seperti sensor suhu dan kelembapan tanah dapat dipasang untuk mengumpulkan data real-time yang memungkinkan petani untuk membuat keputusan yang lebih tepat. Tujuan pengabdian ini dapat mengembangkan sistem monitoring suhu tanah berbasis IoT yang mampu mengumpulkan data secara otomatis dan terus-menerus, serta menampilkan informasi secara langsung melalui tampilan dilayar LCD. Adopsi IoT dalam pertanian juga menyasar pada pengurangan biaya operasional dan penghindaran pemborosan sumber daya. Hasil Pengabdian pemanfaatan IoT dalam sistem monitoring pengukuran suhu tanah tidak hanya meningkatkan produktivitas pertanian, tetapi juga mempromosikan kelestarian sumber daya alam. Hasil implementasi menunjukkan bahwa sistem IoT ini mampu meningkatkan akurasi pengukuran, mengurangi biaya operasional, dan memudahkan pengawasan kondisi tanah secara menyeluruh. Pengembangan sistem ini diharapkan dapat mendukung kegiatan pertanian berkelanjutan dan pengelolaan sumber daya alam secara lebih efektif. Hal ini menjadi langkah penting dalam mengatasi tantangan perubahan iklim dan penurunan kualitas tanah, serta mendukung pertanian berkelanjutan.
Implementation of Ward AHC for Material Clustering Based on Mechanical Parameters Yusuf, Edy; Bakhtiar; Syukriah; Burhanuddin; Riyadhul Fajri
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.977

Abstract

This study aims to implement the Ward Agglomerative Hierarchical Clustering (Ward AHC) algorithm to classify materials based on mechanical parameters, including tensile strength (Su), yield strength (Sy), elastic modulus (E), shear modulus (G), Poisson's ratio (?), and density (?). The clustering results reveal that the data is divided into three main groups with the following distributions: Cluster 1 (321 data points), Cluster 2 (403 data points), and Cluster 3 (828 data points). Each cluster exhibits unique characteristics: Cluster 1 is dominated by materials with low Su and Sy values, moderate E and G values, and light ?. Cluster 2 features materials with very high E values, while Su, Sy, and G values vary. Cluster 3 is characterized by moderate Su values, low Sy values, high E and G values, and light ?. An evaluation using the Silhouette Score yielded a value of 0.492, indicating that the clustering quality is reasonably good, though there is evidence that some data points may lie near the boundaries between clusters.
Klasifikasi Spesies Ikan Koi Berdasarkan Citra Menggunakan Metode YOLOv3-Tiny Dan OpenCV Rauzi Saputra; Imam Muslem; Riyadhul Fajri
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.52

Abstract

Identification of koi fish (Cyprinus carpio) varieties in aquaculture and ornamental fish industries is commonly performed manually through visual observation, making the process subjective, inconsistent, and inefficient, particularly at large production scales. This study aims to develop an automated image-based detection and classification system for koi varieties using the YOLOv3-Tiny algorithm integrated with OpenCV, capable of operating in real-time conditions. The dataset consists of 3,154 images of six koi varieties—Asagi, Bekko, Hikarimono, Kohaku, Sanke, and Showa—which were expanded to 6,360 images through data augmentation techniques. Image labeling and annotation were conducted using Roboflow, while model training was implemented with the Darknet framework in a Google Colab environment supported by GPU acceleration. System performance was evaluated using mean Average Precision (mAP), loss function analysis, and both static image and real-time video testing. Experimental results demonstrate that the YOLOv3-Tiny model is capable of accurately detecting and classifying koi varieties with stable inference speed suitable for real-time applications. The proposed system enhances objectivity, consistency, and efficiency in koi variety identification and shows strong potential for practical implementation in technology-driven ornamental fish farming and trading industries
Co-Authors Bakhtiar Barokah, Muhammad Burhanuddin Iqbal Muslem R, Imam Rauzi Saputra Salamah Syukriah Yusuf, Edy